eReefs Xarray interactiveΒΆ
import os
import numpy as np
import pandas as pd
import xarray as xr
import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
cartopy.config['data_dir'] = os.getenv('CARTOPY_DIR', cartopy.config.get('data_dir'))
import cmocean
import hvplot.xarray
import holoviews as hv
from holoviews import opts, dim
import geoviews as gv
import geoviews.feature as gf
from geoviews import tile_sources as gvts
from cartopy import crs
gv.extension('bokeh')
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
year = 2018
base_url = "http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/gbr4_v2/daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-"
hydrofiles = [f"{base_url}{year}-{month:02}.nc" for month in range(3, 5)]
hydrofiles
['http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/gbr4_v2/daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2018-03.nc',
'http://thredds.ereefs.aims.gov.au/thredds/dodsC/s3://aims-ereefs-public-prod/derived/ncaggregate/ereefs/gbr4_v2/daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2018-04.nc']
ds_hydro = xr.open_mfdataset(hydrofiles)
ds_hydro
<xarray.Dataset>
Dimensions: (k: 17, latitude: 723, longitude: 491, time: 61)
Coordinates:
* time (time) datetime64[ns] 2018-02-28T14:00:00 ... 2018-04-29T14:...
zc (k) float64 dask.array<chunksize=(17,), meta=np.ndarray>
* latitude (latitude) float64 -28.7 -28.67 -28.64 ... -7.096 -7.066 -7.036
* longitude (longitude) float64 142.2 142.2 142.2 ... 156.8 156.8 156.9
Dimensions without coordinates: k
Data variables:
mean_cur (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
salt (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
temp (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
u (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
v (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
mean_wspeed (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
eta (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
wspeed_u (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
wspeed_v (time, latitude, longitude) float32 dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
Attributes: (12/21)
Conventions: CF-1.0
NCO: 4.4.4
Run_ID: 2
_CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
aims_ncaggregate_buildDate: 2020-08-21T14:27:56+10:00
aims_ncaggregate_datasetId: products__ncaggregate__ereefs__gbr4_v2__...
... ...
paramhead: GBR 4km resolution grid
shoc_version: v1.1 rev(5620)
technical_guide_link: https://eatlas.org.au/pydio/public/aims-...
technical_guide_publish_date: 2020-08-18
title: eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 d...
DODS_EXTRA.Unlimited_Dimension: timexarray.Dataset
- k: 17
- latitude: 723
- longitude: 491
- time: 61
- time(time)datetime64[ns]2018-02-28T14:00:00 ... 2018-04-...
- long_name :
- Time
- standard_name :
- time
- coordinate_type :
- time
- _CoordinateAxisType :
- Time
- _ChunkSizes :
- 1024
array(['2018-02-28T14:00:00.000000000', '2018-03-01T14:00:00.000000000', '2018-03-02T14:00:00.000000000', '2018-03-03T14:00:00.000000000', '2018-03-04T14:00:00.000000000', '2018-03-05T14:00:00.000000000', '2018-03-06T14:00:00.000000000', '2018-03-07T14:00:00.000000000', '2018-03-08T14:00:00.000000000', '2018-03-09T14:00:00.000000000', '2018-03-10T14:00:00.000000000', '2018-03-11T14:00:00.000000000', '2018-03-12T14:00:00.000000000', '2018-03-13T14:00:00.000000000', '2018-03-14T14:00:00.000000000', '2018-03-15T14:00:00.000000000', '2018-03-16T14:00:00.000000000', '2018-03-17T14:00:00.000000000', '2018-03-18T14:00:00.000000000', '2018-03-19T14:00:00.000000000', '2018-03-20T14:00:00.000000000', '2018-03-21T14:00:00.000000000', '2018-03-22T14:00:00.000000000', '2018-03-23T14:00:00.000000000', '2018-03-24T14:00:00.000000000', '2018-03-25T14:00:00.000000000', '2018-03-26T14:00:00.000000000', '2018-03-27T14:00:00.000000000', '2018-03-28T14:00:00.000000000', '2018-03-29T14:00:00.000000000', '2018-03-30T14:00:00.000000000', '2018-03-31T14:00:00.000000000', '2018-04-01T14:00:00.000000000', '2018-04-02T14:00:00.000000000', '2018-04-03T14:00:00.000000000', '2018-04-04T14:00:00.000000000', '2018-04-05T14:00:00.000000000', '2018-04-06T14:00:00.000000000', '2018-04-07T14:00:00.000000000', '2018-04-08T14:00:00.000000000', '2018-04-09T14:00:00.000000000', '2018-04-10T14:00:00.000000000', '2018-04-11T14:00:00.000000000', '2018-04-12T14:00:00.000000000', '2018-04-13T14:00:00.000000000', '2018-04-14T14:00:00.000000000', '2018-04-15T14:00:00.000000000', '2018-04-16T14:00:00.000000000', '2018-04-17T14:00:00.000000000', '2018-04-18T14:00:00.000000000', '2018-04-19T14:00:00.000000000', '2018-04-20T14:00:00.000000000', '2018-04-21T14:00:00.000000000', '2018-04-22T14:00:00.000000000', '2018-04-23T14:00:00.000000000', '2018-04-24T14:00:00.000000000', '2018-04-25T14:00:00.000000000', '2018-04-26T14:00:00.000000000', '2018-04-27T14:00:00.000000000', '2018-04-28T14:00:00.000000000', '2018-04-29T14:00:00.000000000'], dtype='datetime64[ns]') - zc(k)float64dask.array<chunksize=(17,), meta=np.ndarray>
- units :
- m
- positive :
- up
- long_name :
- Z coordinate
- axis :
- Z
- coordinate_type :
- Z
- _CoordinateAxisType :
- Height
- _CoordinateZisPositive :
- up
Array Chunk Bytes 136 B 136 B Shape (17,) (17,) Count 5 Tasks 1 Chunks Type float64 numpy.ndarray - latitude(latitude)float64-28.7 -28.67 ... -7.066 -7.036
- units :
- degrees_north
- long_name :
- Latitude
- standard_name :
- latitude
- coordinate_type :
- latitude
- projection :
- geographic
- _CoordinateAxisType :
- Lat
array([-28.696022, -28.666022, -28.636022, ..., -7.096022, -7.066022, -7.036022]) - longitude(longitude)float64142.2 142.2 142.2 ... 156.8 156.9
- units :
- degrees_east
- long_name :
- Longitude
- standard_name :
- longitude
- coordinate_type :
- longitude
- projection :
- geographic
- _CoordinateAxisType :
- Lon
array([142.168788, 142.198788, 142.228788, ..., 156.808788, 156.838788, 156.868788])
- mean_cur(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- short_name :
- mean_cur
- aggregation :
- mean_speed
- standard_name :
- mean_current_speed
- long_name :
- mean_current_speed
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 1.47 GB 748.33 MB Shape (61, 17, 723, 491) (31, 17, 723, 491) Count 6 Tasks 2 Chunks Type float32 numpy.ndarray - salt(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- substanceOrTaxon_id :
- http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/practical_salinity
- short_name :
- salt
- aggregation :
- Daily
- units :
- PSU
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://environment.data.gov.au/water/quality/def/unit/PSU
- long_name :
- Salinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 1.47 GB 748.33 MB Shape (61, 17, 723, 491) (31, 17, 723, 491) Count 6 Tasks 2 Chunks Type float32 numpy.ndarray - temp(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- substanceOrTaxon_id :
- http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_temperature
- short_name :
- temp
- aggregation :
- Daily
- units :
- degrees C
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#DegreeCelsius
- long_name :
- Temperature
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 1.47 GB 748.33 MB Shape (61, 17, 723, 491) (31, 17, 723, 491) Count 6 Tasks 2 Chunks Type float32 numpy.ndarray - u(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- vector_components :
- u v
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_velocity_eastward
- short_name :
- u
- vector_name :
- Currents
- standard_name :
- eastward_sea_water_velocity
- aggregation :
- Daily
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- long_name :
- Eastward current
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 1.47 GB 748.33 MB Shape (61, 17, 723, 491) (31, 17, 723, 491) Count 6 Tasks 2 Chunks Type float32 numpy.ndarray - v(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 723, 491), meta=np.ndarray>
- vector_components :
- u v
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_velocity_northward
- short_name :
- v
- vector_name :
- Currents
- standard_name :
- northward_sea_water_velocity
- aggregation :
- Daily
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- long_name :
- Northward current
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 1.47 GB 748.33 MB Shape (61, 17, 723, 491) (31, 17, 723, 491) Count 6 Tasks 2 Chunks Type float32 numpy.ndarray - mean_wspeed(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- units :
- ms-1
- short_name :
- mean_wspeed
- aggregation :
- mean_speed
- standard_name :
- mean_wind_speed
- long_name :
- mean_wind_speed
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 86.62 MB 44.02 MB Shape (61, 723, 491) (31, 723, 491) Count 6 Tasks 2 Chunks Type float32 numpy.ndarray - eta(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_near_surface
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_surface_elevation
- short_name :
- eta
- standard_name :
- sea_surface_height_above_sea_level
- aggregation :
- Daily
- units :
- metre
- positive :
- up
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#Meter
- long_name :
- Surface elevation
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 86.62 MB 44.02 MB Shape (61, 723, 491) (31, 723, 491) Count 6 Tasks 2 Chunks Type float32 numpy.ndarray - wspeed_u(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- wspeed_u
- aggregation :
- Daily
- units :
- ms-1
- long_name :
- eastward_wind
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 86.62 MB 44.02 MB Shape (61, 723, 491) (31, 723, 491) Count 6 Tasks 2 Chunks Type float32 numpy.ndarray - wspeed_v(time, latitude, longitude)float32dask.array<chunksize=(31, 723, 491), meta=np.ndarray>
- short_name :
- wspeed_v
- aggregation :
- Daily
- units :
- ms-1
- long_name :
- northward_wind
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 86.62 MB 44.02 MB Shape (61, 723, 491) (31, 723, 491) Count 6 Tasks 2 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.0
- NCO :
- 4.4.4
- Run_ID :
- 2
- _CoordSysBuilder :
- ucar.nc2.dataset.conv.CF1Convention
- aims_ncaggregate_buildDate :
- 2020-08-21T14:27:56+10:00
- aims_ncaggregate_datasetId :
- products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2018-03
- aims_ncaggregate_firstDate :
- 2018-03-01T00:00:00+10:00
- aims_ncaggregate_inputs :
- [products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2018-03::MD5:d38e2cde5a55d183aa98d84609b3636a]
- aims_ncaggregate_lastDate :
- 2018-03-31T00:00:00+10:00
- description :
- Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.
- hasVocab :
- 1
- history :
- Fri Nov 15 11:29:35 2019: ncrcat -o ../gbr4_simple_2018-03.nc gbr4_simple_2018-03-0.nc gbr4_simple_2018-03-1.nc Thu Nov 14 17:41:17 2019: ncks -4 -L 3 -d time,0,624 gbr4_simple_2018-03.nc gbr4_simple_2018-03-0.nc 2020-08-20T14:55:54+10:00: vendor: AIMS; processing: None summaries 2020-08-21T14:27:56+10:00: vendor: AIMS; processing: Daily summaries
- metadata_link :
- https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e
- nco_openmp_thread_number :
- 1
- paramfile :
- in.prm
- paramhead :
- GBR 4km resolution grid
- shoc_version :
- v1.1 rev(5620)
- technical_guide_link :
- https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf
- technical_guide_publish_date :
- 2020-08-18
- title :
- eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation
- DODS_EXTRA.Unlimited_Dimension :
- time
reef_lat = -18.82
reef_lon = 147.64
min_lon = 146.5
min_lat = -20
max_lon = 148
max_lat = -17
lon_bnds = [min_lon, max_lon]
lat_bnds = [min_lat, max_lat]
ds_hydro_clip = ds_hydro.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))
ds_hydro_clip.coords['k'] = ('zc',ds_hydro_clip.zc)
ds_hydro_clip = ds_hydro_clip.swap_dims({'zc':'k'})
ds_hydro_clip = ds_hydro_clip.drop(['zc'])
ds_hydro_clip
<xarray.Dataset>
Dimensions: (k: 17, latitude: 100, longitude: 50, time: 61)
Coordinates:
* time (time) datetime64[ns] 2018-02-28T14:00:00 ... 2018-04-29T14:...
* latitude (latitude) float64 -20.0 -19.97 -19.94 ... -17.09 -17.06 -17.03
* longitude (longitude) float64 146.5 146.5 146.6 ... 147.9 148.0 148.0
* k (k) float64 -145.0 -120.0 -103.0 -88.0 ... -5.55 -3.0 -1.5 -0.5
Data variables:
mean_cur (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 100, 50), meta=np.ndarray>
salt (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 100, 50), meta=np.ndarray>
temp (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 100, 50), meta=np.ndarray>
u (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 100, 50), meta=np.ndarray>
v (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 17, 100, 50), meta=np.ndarray>
mean_wspeed (time, latitude, longitude) float32 dask.array<chunksize=(31, 100, 50), meta=np.ndarray>
eta (time, latitude, longitude) float32 dask.array<chunksize=(31, 100, 50), meta=np.ndarray>
wspeed_u (time, latitude, longitude) float32 dask.array<chunksize=(31, 100, 50), meta=np.ndarray>
wspeed_v (time, latitude, longitude) float32 dask.array<chunksize=(31, 100, 50), meta=np.ndarray>
Attributes: (12/21)
Conventions: CF-1.0
NCO: 4.4.4
Run_ID: 2
_CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
aims_ncaggregate_buildDate: 2020-08-21T14:27:56+10:00
aims_ncaggregate_datasetId: products__ncaggregate__ereefs__gbr4_v2__...
... ...
paramhead: GBR 4km resolution grid
shoc_version: v1.1 rev(5620)
technical_guide_link: https://eatlas.org.au/pydio/public/aims-...
technical_guide_publish_date: 2020-08-18
title: eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 d...
DODS_EXTRA.Unlimited_Dimension: timexarray.Dataset
- k: 17
- latitude: 100
- longitude: 50
- time: 61
- time(time)datetime64[ns]2018-02-28T14:00:00 ... 2018-04-...
- long_name :
- Time
- standard_name :
- time
- coordinate_type :
- time
- _CoordinateAxisType :
- Time
- _ChunkSizes :
- 1024
array(['2018-02-28T14:00:00.000000000', '2018-03-01T14:00:00.000000000', '2018-03-02T14:00:00.000000000', '2018-03-03T14:00:00.000000000', '2018-03-04T14:00:00.000000000', '2018-03-05T14:00:00.000000000', '2018-03-06T14:00:00.000000000', '2018-03-07T14:00:00.000000000', '2018-03-08T14:00:00.000000000', '2018-03-09T14:00:00.000000000', '2018-03-10T14:00:00.000000000', '2018-03-11T14:00:00.000000000', '2018-03-12T14:00:00.000000000', '2018-03-13T14:00:00.000000000', '2018-03-14T14:00:00.000000000', '2018-03-15T14:00:00.000000000', '2018-03-16T14:00:00.000000000', '2018-03-17T14:00:00.000000000', '2018-03-18T14:00:00.000000000', '2018-03-19T14:00:00.000000000', '2018-03-20T14:00:00.000000000', '2018-03-21T14:00:00.000000000', '2018-03-22T14:00:00.000000000', '2018-03-23T14:00:00.000000000', '2018-03-24T14:00:00.000000000', '2018-03-25T14:00:00.000000000', '2018-03-26T14:00:00.000000000', '2018-03-27T14:00:00.000000000', '2018-03-28T14:00:00.000000000', '2018-03-29T14:00:00.000000000', '2018-03-30T14:00:00.000000000', '2018-03-31T14:00:00.000000000', '2018-04-01T14:00:00.000000000', '2018-04-02T14:00:00.000000000', '2018-04-03T14:00:00.000000000', '2018-04-04T14:00:00.000000000', '2018-04-05T14:00:00.000000000', '2018-04-06T14:00:00.000000000', '2018-04-07T14:00:00.000000000', '2018-04-08T14:00:00.000000000', '2018-04-09T14:00:00.000000000', '2018-04-10T14:00:00.000000000', '2018-04-11T14:00:00.000000000', '2018-04-12T14:00:00.000000000', '2018-04-13T14:00:00.000000000', '2018-04-14T14:00:00.000000000', '2018-04-15T14:00:00.000000000', '2018-04-16T14:00:00.000000000', '2018-04-17T14:00:00.000000000', '2018-04-18T14:00:00.000000000', '2018-04-19T14:00:00.000000000', '2018-04-20T14:00:00.000000000', '2018-04-21T14:00:00.000000000', '2018-04-22T14:00:00.000000000', '2018-04-23T14:00:00.000000000', '2018-04-24T14:00:00.000000000', '2018-04-25T14:00:00.000000000', '2018-04-26T14:00:00.000000000', '2018-04-27T14:00:00.000000000', '2018-04-28T14:00:00.000000000', '2018-04-29T14:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float64-20.0 -19.97 ... -17.06 -17.03
- units :
- degrees_north
- long_name :
- Latitude
- standard_name :
- latitude
- coordinate_type :
- latitude
- projection :
- geographic
- _CoordinateAxisType :
- Lat
array([-19.996022, -19.966022, -19.936022, -19.906022, -19.876022, -19.846022, -19.816022, -19.786022, -19.756022, -19.726022, -19.696022, -19.666022, -19.636022, -19.606022, -19.576022, -19.546022, -19.516022, -19.486022, -19.456022, -19.426022, -19.396022, -19.366022, -19.336022, -19.306022, -19.276022, -19.246022, -19.216022, -19.186022, -19.156022, -19.126022, -19.096022, -19.066022, -19.036022, -19.006022, -18.976022, -18.946022, -18.916022, -18.886022, -18.856022, -18.826022, -18.796022, -18.766022, -18.736022, -18.706022, -18.676022, -18.646022, -18.616022, -18.586022, -18.556022, -18.526022, -18.496022, -18.466022, -18.436022, -18.406022, -18.376022, -18.346022, -18.316022, -18.286022, -18.256022, -18.226022, -18.196022, -18.166022, -18.136022, -18.106022, -18.076022, -18.046022, -18.016022, -17.986022, -17.956022, -17.926022, -17.896022, -17.866022, -17.836022, -17.806022, -17.776022, -17.746022, -17.716022, -17.686022, -17.656022, -17.626022, -17.596022, -17.566022, -17.536022, -17.506022, -17.476022, -17.446022, -17.416022, -17.386022, -17.356022, -17.326022, -17.296022, -17.266022, -17.236022, -17.206022, -17.176022, -17.146022, -17.116022, -17.086022, -17.056022, -17.026022]) - longitude(longitude)float64146.5 146.5 146.6 ... 148.0 148.0
- units :
- degrees_east
- long_name :
- Longitude
- standard_name :
- longitude
- coordinate_type :
- longitude
- projection :
- geographic
- _CoordinateAxisType :
- Lon
array([146.518788, 146.548788, 146.578788, 146.608788, 146.638788, 146.668788, 146.698788, 146.728788, 146.758788, 146.788788, 146.818788, 146.848788, 146.878788, 146.908788, 146.938788, 146.968788, 146.998788, 147.028788, 147.058788, 147.088788, 147.118788, 147.148788, 147.178788, 147.208788, 147.238788, 147.268788, 147.298788, 147.328788, 147.358788, 147.388788, 147.418788, 147.448788, 147.478788, 147.508788, 147.538788, 147.568788, 147.598788, 147.628788, 147.658788, 147.688788, 147.718788, 147.748788, 147.778788, 147.808788, 147.838788, 147.868788, 147.898788, 147.928788, 147.958788, 147.988788]) - k(k)float64-145.0 -120.0 -103.0 ... -1.5 -0.5
array([-145. , -120. , -103. , -88. , -73. , -60. , -49. , -39.5 , -31. , -23.75, -17.75, -12.75, -8.8 , -5.55, -3. , -1.5 , -0.5 ])
- mean_cur(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 100, 50), meta=np.ndarray>
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- short_name :
- mean_cur
- aggregation :
- mean_speed
- standard_name :
- mean_current_speed
- long_name :
- mean_current_speed
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 20.74 MB 10.54 MB Shape (61, 17, 100, 50) (31, 17, 100, 50) Count 8 Tasks 2 Chunks Type float32 numpy.ndarray - salt(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 100, 50), meta=np.ndarray>
- substanceOrTaxon_id :
- http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/practical_salinity
- short_name :
- salt
- aggregation :
- Daily
- units :
- PSU
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://environment.data.gov.au/water/quality/def/unit/PSU
- long_name :
- Salinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 20.74 MB 10.54 MB Shape (61, 17, 100, 50) (31, 17, 100, 50) Count 8 Tasks 2 Chunks Type float32 numpy.ndarray - temp(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 100, 50), meta=np.ndarray>
- substanceOrTaxon_id :
- http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_temperature
- short_name :
- temp
- aggregation :
- Daily
- units :
- degrees C
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#DegreeCelsius
- long_name :
- Temperature
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 20.74 MB 10.54 MB Shape (61, 17, 100, 50) (31, 17, 100, 50) Count 8 Tasks 2 Chunks Type float32 numpy.ndarray - u(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 100, 50), meta=np.ndarray>
- vector_components :
- u v
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_velocity_eastward
- short_name :
- u
- vector_name :
- Currents
- standard_name :
- eastward_sea_water_velocity
- aggregation :
- Daily
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- long_name :
- Eastward current
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 20.74 MB 10.54 MB Shape (61, 17, 100, 50) (31, 17, 100, 50) Count 8 Tasks 2 Chunks Type float32 numpy.ndarray - v(time, k, latitude, longitude)float32dask.array<chunksize=(31, 17, 100, 50), meta=np.ndarray>
- vector_components :
- u v
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_velocity_northward
- short_name :
- v
- vector_name :
- Currents
- standard_name :
- northward_sea_water_velocity
- aggregation :
- Daily
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- long_name :
- Northward current
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 20.74 MB 10.54 MB Shape (61, 17, 100, 50) (31, 17, 100, 50) Count 8 Tasks 2 Chunks Type float32 numpy.ndarray - mean_wspeed(time, latitude, longitude)float32dask.array<chunksize=(31, 100, 50), meta=np.ndarray>
- units :
- ms-1
- short_name :
- mean_wspeed
- aggregation :
- mean_speed
- standard_name :
- mean_wind_speed
- long_name :
- mean_wind_speed
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 1.22 MB 620.00 kB Shape (61, 100, 50) (31, 100, 50) Count 8 Tasks 2 Chunks Type float32 numpy.ndarray - eta(time, latitude, longitude)float32dask.array<chunksize=(31, 100, 50), meta=np.ndarray>
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_near_surface
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_surface_elevation
- short_name :
- eta
- standard_name :
- sea_surface_height_above_sea_level
- aggregation :
- Daily
- units :
- metre
- positive :
- up
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#Meter
- long_name :
- Surface elevation
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 1.22 MB 620.00 kB Shape (61, 100, 50) (31, 100, 50) Count 8 Tasks 2 Chunks Type float32 numpy.ndarray - wspeed_u(time, latitude, longitude)float32dask.array<chunksize=(31, 100, 50), meta=np.ndarray>
- short_name :
- wspeed_u
- aggregation :
- Daily
- units :
- ms-1
- long_name :
- eastward_wind
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 1.22 MB 620.00 kB Shape (61, 100, 50) (31, 100, 50) Count 8 Tasks 2 Chunks Type float32 numpy.ndarray - wspeed_v(time, latitude, longitude)float32dask.array<chunksize=(31, 100, 50), meta=np.ndarray>
- short_name :
- wspeed_v
- aggregation :
- Daily
- units :
- ms-1
- long_name :
- northward_wind
- _ChunkSizes :
- [ 1 133 491]
Array Chunk Bytes 1.22 MB 620.00 kB Shape (61, 100, 50) (31, 100, 50) Count 8 Tasks 2 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.0
- NCO :
- 4.4.4
- Run_ID :
- 2
- _CoordSysBuilder :
- ucar.nc2.dataset.conv.CF1Convention
- aims_ncaggregate_buildDate :
- 2020-08-21T14:27:56+10:00
- aims_ncaggregate_datasetId :
- products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2018-03
- aims_ncaggregate_firstDate :
- 2018-03-01T00:00:00+10:00
- aims_ncaggregate_inputs :
- [products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2018-03::MD5:d38e2cde5a55d183aa98d84609b3636a]
- aims_ncaggregate_lastDate :
- 2018-03-31T00:00:00+10:00
- description :
- Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.
- hasVocab :
- 1
- history :
- Fri Nov 15 11:29:35 2019: ncrcat -o ../gbr4_simple_2018-03.nc gbr4_simple_2018-03-0.nc gbr4_simple_2018-03-1.nc Thu Nov 14 17:41:17 2019: ncks -4 -L 3 -d time,0,624 gbr4_simple_2018-03.nc gbr4_simple_2018-03-0.nc 2020-08-20T14:55:54+10:00: vendor: AIMS; processing: None summaries 2020-08-21T14:27:56+10:00: vendor: AIMS; processing: Daily summaries
- metadata_link :
- https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e
- nco_openmp_thread_number :
- 1
- paramfile :
- in.prm
- paramhead :
- GBR 4km resolution grid
- shoc_version :
- v1.1 rev(5620)
- technical_guide_link :
- https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf
- technical_guide_publish_date :
- 2020-08-18
- title :
- eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation
- DODS_EXTRA.Unlimited_Dimension :
- time
# Take the top 10 m
slice_ds = ds_hydro_clip.sel(k=slice(-10, 0)).drop(['u','v','eta','mean_wspeed','wspeed_u','wspeed_v'])
slice_ds
<xarray.Dataset>
Dimensions: (k: 5, latitude: 100, longitude: 50, time: 61)
Coordinates:
* time (time) datetime64[ns] 2018-02-28T14:00:00 ... 2018-04-29T14:00:00
* latitude (latitude) float64 -20.0 -19.97 -19.94 ... -17.09 -17.06 -17.03
* longitude (longitude) float64 146.5 146.5 146.6 146.6 ... 147.9 148.0 148.0
* k (k) float64 -8.8 -5.55 -3.0 -1.5 -0.5
Data variables:
mean_cur (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 5, 100, 50), meta=np.ndarray>
salt (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 5, 100, 50), meta=np.ndarray>
temp (time, k, latitude, longitude) float32 dask.array<chunksize=(31, 5, 100, 50), meta=np.ndarray>
Attributes: (12/21)
Conventions: CF-1.0
NCO: 4.4.4
Run_ID: 2
_CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention
aims_ncaggregate_buildDate: 2020-08-21T14:27:56+10:00
aims_ncaggregate_datasetId: products__ncaggregate__ereefs__gbr4_v2__...
... ...
paramhead: GBR 4km resolution grid
shoc_version: v1.1 rev(5620)
technical_guide_link: https://eatlas.org.au/pydio/public/aims-...
technical_guide_publish_date: 2020-08-18
title: eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 d...
DODS_EXTRA.Unlimited_Dimension: timexarray.Dataset
- k: 5
- latitude: 100
- longitude: 50
- time: 61
- time(time)datetime64[ns]2018-02-28T14:00:00 ... 2018-04-...
- long_name :
- Time
- standard_name :
- time
- coordinate_type :
- time
- _CoordinateAxisType :
- Time
- _ChunkSizes :
- 1024
array(['2018-02-28T14:00:00.000000000', '2018-03-01T14:00:00.000000000', '2018-03-02T14:00:00.000000000', '2018-03-03T14:00:00.000000000', '2018-03-04T14:00:00.000000000', '2018-03-05T14:00:00.000000000', '2018-03-06T14:00:00.000000000', '2018-03-07T14:00:00.000000000', '2018-03-08T14:00:00.000000000', '2018-03-09T14:00:00.000000000', '2018-03-10T14:00:00.000000000', '2018-03-11T14:00:00.000000000', '2018-03-12T14:00:00.000000000', '2018-03-13T14:00:00.000000000', '2018-03-14T14:00:00.000000000', '2018-03-15T14:00:00.000000000', '2018-03-16T14:00:00.000000000', '2018-03-17T14:00:00.000000000', '2018-03-18T14:00:00.000000000', '2018-03-19T14:00:00.000000000', '2018-03-20T14:00:00.000000000', '2018-03-21T14:00:00.000000000', '2018-03-22T14:00:00.000000000', '2018-03-23T14:00:00.000000000', '2018-03-24T14:00:00.000000000', '2018-03-25T14:00:00.000000000', '2018-03-26T14:00:00.000000000', '2018-03-27T14:00:00.000000000', '2018-03-28T14:00:00.000000000', '2018-03-29T14:00:00.000000000', '2018-03-30T14:00:00.000000000', '2018-03-31T14:00:00.000000000', '2018-04-01T14:00:00.000000000', '2018-04-02T14:00:00.000000000', '2018-04-03T14:00:00.000000000', '2018-04-04T14:00:00.000000000', '2018-04-05T14:00:00.000000000', '2018-04-06T14:00:00.000000000', '2018-04-07T14:00:00.000000000', '2018-04-08T14:00:00.000000000', '2018-04-09T14:00:00.000000000', '2018-04-10T14:00:00.000000000', '2018-04-11T14:00:00.000000000', '2018-04-12T14:00:00.000000000', '2018-04-13T14:00:00.000000000', '2018-04-14T14:00:00.000000000', '2018-04-15T14:00:00.000000000', '2018-04-16T14:00:00.000000000', '2018-04-17T14:00:00.000000000', '2018-04-18T14:00:00.000000000', '2018-04-19T14:00:00.000000000', '2018-04-20T14:00:00.000000000', '2018-04-21T14:00:00.000000000', '2018-04-22T14:00:00.000000000', '2018-04-23T14:00:00.000000000', '2018-04-24T14:00:00.000000000', '2018-04-25T14:00:00.000000000', '2018-04-26T14:00:00.000000000', '2018-04-27T14:00:00.000000000', '2018-04-28T14:00:00.000000000', '2018-04-29T14:00:00.000000000'], dtype='datetime64[ns]') - latitude(latitude)float64-20.0 -19.97 ... -17.06 -17.03
- units :
- degrees_north
- long_name :
- Latitude
- standard_name :
- latitude
- coordinate_type :
- latitude
- projection :
- geographic
- _CoordinateAxisType :
- Lat
array([-19.996022, -19.966022, -19.936022, -19.906022, -19.876022, -19.846022, -19.816022, -19.786022, -19.756022, -19.726022, -19.696022, -19.666022, -19.636022, -19.606022, -19.576022, -19.546022, -19.516022, -19.486022, -19.456022, -19.426022, -19.396022, -19.366022, -19.336022, -19.306022, -19.276022, -19.246022, -19.216022, -19.186022, -19.156022, -19.126022, -19.096022, -19.066022, -19.036022, -19.006022, -18.976022, -18.946022, -18.916022, -18.886022, -18.856022, -18.826022, -18.796022, -18.766022, -18.736022, -18.706022, -18.676022, -18.646022, -18.616022, -18.586022, -18.556022, -18.526022, -18.496022, -18.466022, -18.436022, -18.406022, -18.376022, -18.346022, -18.316022, -18.286022, -18.256022, -18.226022, -18.196022, -18.166022, -18.136022, -18.106022, -18.076022, -18.046022, -18.016022, -17.986022, -17.956022, -17.926022, -17.896022, -17.866022, -17.836022, -17.806022, -17.776022, -17.746022, -17.716022, -17.686022, -17.656022, -17.626022, -17.596022, -17.566022, -17.536022, -17.506022, -17.476022, -17.446022, -17.416022, -17.386022, -17.356022, -17.326022, -17.296022, -17.266022, -17.236022, -17.206022, -17.176022, -17.146022, -17.116022, -17.086022, -17.056022, -17.026022]) - longitude(longitude)float64146.5 146.5 146.6 ... 148.0 148.0
- units :
- degrees_east
- long_name :
- Longitude
- standard_name :
- longitude
- coordinate_type :
- longitude
- projection :
- geographic
- _CoordinateAxisType :
- Lon
array([146.518788, 146.548788, 146.578788, 146.608788, 146.638788, 146.668788, 146.698788, 146.728788, 146.758788, 146.788788, 146.818788, 146.848788, 146.878788, 146.908788, 146.938788, 146.968788, 146.998788, 147.028788, 147.058788, 147.088788, 147.118788, 147.148788, 147.178788, 147.208788, 147.238788, 147.268788, 147.298788, 147.328788, 147.358788, 147.388788, 147.418788, 147.448788, 147.478788, 147.508788, 147.538788, 147.568788, 147.598788, 147.628788, 147.658788, 147.688788, 147.718788, 147.748788, 147.778788, 147.808788, 147.838788, 147.868788, 147.898788, 147.928788, 147.958788, 147.988788]) - k(k)float64-8.8 -5.55 -3.0 -1.5 -0.5
array([-8.8 , -5.55, -3. , -1.5 , -0.5 ])
- mean_cur(time, k, latitude, longitude)float32dask.array<chunksize=(31, 5, 100, 50), meta=np.ndarray>
- substanceOrTaxon_id :
- http://environment.data.gov.au/def/feature/ocean_current
- units :
- ms-1
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#MeterPerSecond
- short_name :
- mean_cur
- aggregation :
- mean_speed
- standard_name :
- mean_current_speed
- long_name :
- mean_current_speed
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 6.10 MB 3.10 MB Shape (61, 5, 100, 50) (31, 5, 100, 50) Count 10 Tasks 2 Chunks Type float32 numpy.ndarray - salt(time, k, latitude, longitude)float32dask.array<chunksize=(31, 5, 100, 50), meta=np.ndarray>
- substanceOrTaxon_id :
- http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/practical_salinity
- short_name :
- salt
- aggregation :
- Daily
- units :
- PSU
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://environment.data.gov.au/water/quality/def/unit/PSU
- long_name :
- Salinity
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 6.10 MB 3.10 MB Shape (61, 5, 100, 50) (31, 5, 100, 50) Count 10 Tasks 2 Chunks Type float32 numpy.ndarray - temp(time, k, latitude, longitude)float32dask.array<chunksize=(31, 5, 100, 50), meta=np.ndarray>
- substanceOrTaxon_id :
- http://sweet.jpl.nasa.gov/2.2/matrWater.owl#SaltWater
- scaledQuantityKind_id :
- http://environment.data.gov.au/def/property/sea_water_temperature
- short_name :
- temp
- aggregation :
- Daily
- units :
- degrees C
- medium_id :
- http://environment.data.gov.au/def/feature/ocean
- unit_id :
- http://qudt.org/vocab/unit#DegreeCelsius
- long_name :
- Temperature
- _ChunkSizes :
- [ 1 1 133 491]
Array Chunk Bytes 6.10 MB 3.10 MB Shape (61, 5, 100, 50) (31, 5, 100, 50) Count 10 Tasks 2 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.0
- NCO :
- 4.4.4
- Run_ID :
- 2
- _CoordSysBuilder :
- ucar.nc2.dataset.conv.CF1Convention
- aims_ncaggregate_buildDate :
- 2020-08-21T14:27:56+10:00
- aims_ncaggregate_datasetId :
- products__ncaggregate__ereefs__gbr4_v2__daily-monthly/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_daily-monthly-2018-03
- aims_ncaggregate_firstDate :
- 2018-03-01T00:00:00+10:00
- aims_ncaggregate_inputs :
- [products__ncaggregate__ereefs__gbr4_v2__raw/EREEFS_AIMS-CSIRO_gbr4_v2_hydro_raw_2018-03::MD5:d38e2cde5a55d183aa98d84609b3636a]
- aims_ncaggregate_lastDate :
- 2018-03-31T00:00:00+10:00
- description :
- Aggregation of raw hourly input data (from eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 subset) to daily means. Also calculates mean magnitude of wind and ocean current speeds. Data is regridded from curvilinear (per input data) to rectilinear via inverse weighted distance from up to 4 closest cells.
- hasVocab :
- 1
- history :
- Fri Nov 15 11:29:35 2019: ncrcat -o ../gbr4_simple_2018-03.nc gbr4_simple_2018-03-0.nc gbr4_simple_2018-03-1.nc Thu Nov 14 17:41:17 2019: ncks -4 -L 3 -d time,0,624 gbr4_simple_2018-03.nc gbr4_simple_2018-03-0.nc 2020-08-20T14:55:54+10:00: vendor: AIMS; processing: None summaries 2020-08-21T14:27:56+10:00: vendor: AIMS; processing: Daily summaries
- metadata_link :
- https://eatlas.org.au/data/uuid/350aed53-ae0f-436e-9866-d34db7f04d2e
- nco_openmp_thread_number :
- 1
- paramfile :
- in.prm
- paramhead :
- GBR 4km resolution grid
- shoc_version :
- v1.1 rev(5620)
- technical_guide_link :
- https://eatlas.org.au/pydio/public/aims-ereefs-platform-technical-guide-to-derived-products-from-csiro-ereefs-models-pdf
- technical_guide_publish_date :
- 2020-08-18
- title :
- eReefs AIMS-CSIRO GBR4 Hydrodynamic v2 daily aggregation
- DODS_EXTRA.Unlimited_Dimension :
- time
vards = slice_ds.mean(dim='k').salt.load()
coastline = gf.coastline(line_width=3, line_color='k').opts(projection=ccrs.PlateCarree(), scale='10m')
land = gf.land.options(scale='10m', fill_color='lightgray')
var_mask = gv.Dataset(vards, crs=crs.PlateCarree())
minvar = vards.min().item()
maxvar = vards.max().item()
var0 = vards.fillna(0)
hv_ds = hv.Dataset(var0)
# Create stack of images grouped by time
im_mask = var_mask.to(gv.Image, ['longitude', 'latitude'], dynamic=True)
hv.output(widget_location='bottom')
label = slice_ds.salt.long_name+' '+slice_ds.salt.units
image = im_mask.opts(active_tools=['wheel_zoom', 'pan'], cmap=cmocean.cm.curl,
colorbar=True, width=450, height=400, clim=(34,36),
title=label) * coastline * land
image
/usr/share/miniconda/envs/envireef/lib/python3.8/site-packages/cartopy/io/__init__.py:260: DownloadWarning: Downloading: https://naciscdn.org/naturalearth/110m/physical/ne_110m_land.zip
warnings.warn('Downloading: {}'.format(url), DownloadWarning)